{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# API 文档介绍（Face++ API）\n",
    "* 本周主要内容：API文档阅读介绍及计算机视觉入门（认知服务）\n",
    "* 22春_API_人工智能与机器学习_week03\n",
    "*  电子讲义设计者：许智超\n",
    "<br/>\n",
    "<br/>\n",
    "\n",
    "<img src=\"https://pic2.zhimg.com/v2-6c478325d02ca3363ec1817a952d5321_r.jpg\" width=800>\n",
    "\n",
    "-----\n",
    "<img src=\"http://coverall1.splunk.com/web_assets/developers/devguide/Essentials02_Splunk_app_architecture.png\" width=700>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 上周回顾\n",
    "\n",
    "## python requests 模块\n",
    "\n",
    "### content作为响应内容\n",
    "* 数据样态？\n",
    "\n",
    "### text检查相应内容\n",
    "* 观察数据？\n",
    "\n",
    "### json()观察结果\n",
    "* 结论？\n",
    "\n",
    "\n",
    "## 实践 face++ 人脸检测/face detect\n",
    "\n",
    "* API实践步骤\n",
    "> 1. 导入需要的requests模块\n",
    "> 2. 输入我们需要API网站注册的API_Key\n",
    "> 3. 目标url [base url]\n",
    "> 4. 沿用API文档的示范代码,准备我们的headers和图片(数据)\n",
    "> 5. 准备symbol ? 后面的数据,这里需要注意,一定要详细阅读API文档中的 “参数功能”,按照要求格式准备payload参数功能可能有:\n",
    ">> 1. 是否必要?必要的一定要准备好\n",
    ">> 2. 选填的一定是功能,要根据功能需求 好好填噢\n",
    "> 6. response响应。注意:\n",
    ">> 1. 详细阅读文档,注意请求方式(GET、POST、DELETE)\n",
    ">> 2. 注意json 和字典的差异 ,str vs dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. url\n",
    "base_url = 'https://api-cn.faceplusplus.com/facepp/v3/detect'\n",
    "\n",
    "\n",
    "# 2. api账户/通行证\n",
    "API_key = '你的KEY'\n",
    "API_sercret = '你的SERCRET'\n",
    "\n",
    "# 3. POST\n",
    "\n",
    "\n",
    "# 4. 所有 API Key 都可以调用本 API。\n",
    "#    其中 calculate_all 和 face_rectangle 参数只有正式 API Key 才能使用，试用 API Key 无法使用。\n",
    "\n",
    "# 5. payload： 酬载, 必要阅读api文档中的 *必选项* 和 *可选性*，及 *参数说明*\n",
    "payload = {\n",
    "    'api_key':API_key,\n",
    "    'api_secret':API_sercret,\n",
    "    'image_url':'https://tse1-mm.cn.bing.net/th/id/OIP-C.HPWFQ7UDQh93EKcDmiUQ_wHaIR?w=176&h=197&c=7&r=0&o=5&dpr=2&pid=1.7'    \n",
    "}\n",
    "\n",
    "r = requests.post(base_url, params = payload)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'request_id': '1647048332,5743d50c-dc1b-4178-890d-7f0f7d7bc4f4',\n",
       " 'time_used': 543,\n",
       " 'faces': [{'face_token': 'b1bf9d1b73776d1331c29f7b51523672',\n",
       "   'face_rectangle': {'top': 108, 'left': 141, 'width': 107, 'height': 107}}],\n",
       " 'image_id': 'JoYdaZ0h1hGb4ih1d2EZVw==',\n",
       " 'face_num': 1}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "face_detect = r.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "face_tokens = face_detect['faces'][0]['face_token']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b1bf9d1b73776d1331c29f7b51523672'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "face_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 人脸分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. url \n",
    "Analyze_base_url = \"https://api-cn.faceplusplus.com/facepp/v3/face/analyze\"\n",
    "\n",
    "# 2. api账户/通行证\n",
    "API_key = '你的KEY'\n",
    "API_sercret = '你的SERCRET'\n",
    "\n",
    "# 3 POST\n",
    "\n",
    "# 4. 权限：所有 API Key 都可以调用本 API\n",
    "\n",
    "# 5. payload： 酬载, 必要阅读api文档中的 *必选项* 和 *可选性*，及 *参数说明*\n",
    "payload = {\n",
    "    'api_key':API_key,\n",
    "    'api_secret':API_sercret,\n",
    "    'face_tokens':face_tokens,\n",
    "    'return_attributes':'gender,age,smiling,headpose,facequality,blur,eyestatus,emotion,beauty,mouthstatus,eyegaze,skinstatus',\n",
    "    'beauty_score_min':0,\n",
    "    'beauty_score_max':100\n",
    "    \n",
    "}\n",
    "\n",
    "# 6. requests\n",
    "\n",
    "r_analyze = requests.post(url = Analyze_base_url, params = payload)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_analyze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'time_used': 212,\n",
       " 'request_id': '1647048849,2c8e9a2a-52b8-44aa-ace0-618cbc694ec8',\n",
       " 'faces': [{'attributes': {'emotion': {'sadness': 0.039,\n",
       "     'neutral': 0.001,\n",
       "     'disgust': 0.191,\n",
       "     'anger': 0.001,\n",
       "     'surprise': 0.031,\n",
       "     'fear': 0.001,\n",
       "     'happiness': 99.736},\n",
       "    'beauty': {'female_score': 58.555, 'male_score': 58.212},\n",
       "    'gender': {'value': 'Male'},\n",
       "    'age': {'value': 53},\n",
       "    'mouthstatus': {'close': 0.0,\n",
       "     'surgical_mask_or_respirator': 0.0,\n",
       "     'open': 100.0,\n",
       "     'other_occlusion': 0.0},\n",
       "    'glass': {'value': 'None'},\n",
       "    'skinstatus': {'dark_circle': 1.554,\n",
       "     'stain': 9.17,\n",
       "     'acne': 1.527,\n",
       "     'health': 11.352},\n",
       "    'headpose': {'yaw_angle': 2.7483263,\n",
       "     'pitch_angle': 14.900063,\n",
       "     'roll_angle': 0.5866398},\n",
       "    'blur': {'blurness': {'threshold': 50.0, 'value': 0.376},\n",
       "     'motionblur': {'threshold': 50.0, 'value': 0.376},\n",
       "     'gaussianblur': {'threshold': 50.0, 'value': 0.376}},\n",
       "    'smile': {'threshold': 50.0, 'value': 100.0},\n",
       "    'eyestatus': {'left_eye_status': {'normal_glass_eye_open': 0.005,\n",
       "      'no_glass_eye_close': 0.0,\n",
       "      'occlusion': 0.0,\n",
       "      'no_glass_eye_open': 99.995,\n",
       "      'normal_glass_eye_close': 0.0,\n",
       "      'dark_glasses': 0.0},\n",
       "     'right_eye_status': {'normal_glass_eye_open': 0.003,\n",
       "      'no_glass_eye_close': 0.0,\n",
       "      'occlusion': 0.001,\n",
       "      'no_glass_eye_open': 99.996,\n",
       "      'normal_glass_eye_close': 0.0,\n",
       "      'dark_glasses': 0.0}},\n",
       "    'facequality': {'threshold': 70.1, 'value': 85.357},\n",
       "    'eyegaze': {'right_eye_gaze': {'position_x_coordinate': 0.494,\n",
       "      'vector_z_component': 0.935,\n",
       "      'vector_x_component': -0.068,\n",
       "      'vector_y_component': 0.348,\n",
       "      'position_y_coordinate': 0.438},\n",
       "     'left_eye_gaze': {'position_x_coordinate': 0.502,\n",
       "      'vector_z_component': 0.942,\n",
       "      'vector_x_component': 0.247,\n",
       "      'vector_y_component': 0.227,\n",
       "      'position_y_coordinate': 0.422}}},\n",
       "   'face_rectangle': {'width': 107, 'top': 108, 'left': 141, 'height': 107},\n",
       "   'face_token': 'b1bf9d1b73776d1331c29f7b51523672'}]}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_analyze.json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>face_token</th>\n",
       "      <td>b1bf9d1b73776d1331c29f7b51523672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.emotion.sadness</th>\n",
       "      <td>0.681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.emotion.neutral</th>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.emotion.disgust</th>\n",
       "      <td>1.213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.emotion.anger</th>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.emotion.surprise</th>\n",
       "      <td>0.009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.emotion.fear</th>\n",
       "      <td>0.112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.emotion.happiness</th>\n",
       "      <td>97.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.beauty.female_score</th>\n",
       "      <td>56.631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.beauty.male_score</th>\n",
       "      <td>56.694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.gender.value</th>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.age.value</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.mouthstatus.close</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.mouthstatus.surgical_mask_or_respirator</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.mouthstatus.open</th>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.mouthstatus.other_occlusion</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.glass.value</th>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.skinstatus.dark_circle</th>\n",
       "      <td>1.982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.skinstatus.stain</th>\n",
       "      <td>12.624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.skinstatus.acne</th>\n",
       "      <td>0.634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.skinstatus.health</th>\n",
       "      <td>9.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.headpose.yaw_angle</th>\n",
       "      <td>0.004189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.headpose.pitch_angle</th>\n",
       "      <td>16.763775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.headpose.roll_angle</th>\n",
       "      <td>2.305076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.blur.blurness.threshold</th>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.blur.blurness.value</th>\n",
       "      <td>0.364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.blur.motionblur.threshold</th>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.blur.motionblur.value</th>\n",
       "      <td>0.364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.blur.gaussianblur.threshold</th>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.blur.gaussianblur.value</th>\n",
       "      <td>0.364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.smile.threshold</th>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.smile.value</th>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyestatus.left_eye_status.normal_glass_eye_open</th>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyestatus.left_eye_status.no_glass_eye_close</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyestatus.left_eye_status.occlusion</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyestatus.left_eye_status.no_glass_eye_open</th>\n",
       "      <td>99.998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyestatus.left_eye_status.normal_glass_eye_close</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyestatus.left_eye_status.dark_glasses</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyestatus.right_eye_status.normal_glass_eye_open</th>\n",
       "      <td>0.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyestatus.right_eye_status.no_glass_eye_close</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyestatus.right_eye_status.occlusion</th>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyestatus.right_eye_status.no_glass_eye_open</th>\n",
       "      <td>99.961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyestatus.right_eye_status.normal_glass_eye_close</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyestatus.right_eye_status.dark_glasses</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.facequality.threshold</th>\n",
       "      <td>70.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.facequality.value</th>\n",
       "      <td>85.658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyegaze.right_eye_gaze.position_x_coordinate</th>\n",
       "      <td>0.468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyegaze.right_eye_gaze.vector_z_component</th>\n",
       "      <td>0.944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyegaze.right_eye_gaze.vector_x_component</th>\n",
       "      <td>-0.054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyegaze.right_eye_gaze.vector_y_component</th>\n",
       "      <td>0.325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyegaze.right_eye_gaze.position_y_coordinate</th>\n",
       "      <td>0.443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyegaze.left_eye_gaze.position_x_coordinate</th>\n",
       "      <td>0.506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyegaze.left_eye_gaze.vector_z_component</th>\n",
       "      <td>0.948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyegaze.left_eye_gaze.vector_x_component</th>\n",
       "      <td>0.168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyegaze.left_eye_gaze.vector_y_component</th>\n",
       "      <td>0.269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attributes.eyegaze.left_eye_gaze.position_y_coordinate</th>\n",
       "      <td>0.436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>face_rectangle.width</th>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>face_rectangle.top</th>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>face_rectangle.left</th>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>face_rectangle.height</th>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                   0\n",
       "face_token                                          b1bf9d1b73776d1331c29f7b51523672\n",
       "attributes.emotion.sadness                                                     0.681\n",
       "attributes.emotion.neutral                                                     0.007\n",
       "attributes.emotion.disgust                                                     1.213\n",
       "attributes.emotion.anger                                                       0.007\n",
       "attributes.emotion.surprise                                                    0.009\n",
       "attributes.emotion.fear                                                        0.112\n",
       "attributes.emotion.happiness                                                   97.97\n",
       "attributes.beauty.female_score                                                56.631\n",
       "attributes.beauty.male_score                                                  56.694\n",
       "attributes.gender.value                                                         Male\n",
       "attributes.age.value                                                              53\n",
       "attributes.mouthstatus.close                                                     0.0\n",
       "attributes.mouthstatus.surgical_mask_or_respirator                               0.0\n",
       "attributes.mouthstatus.open                                                    100.0\n",
       "attributes.mouthstatus.other_occlusion                                           0.0\n",
       "attributes.glass.value                                                          None\n",
       "attributes.skinstatus.dark_circle                                              1.982\n",
       "attributes.skinstatus.stain                                                   12.624\n",
       "attributes.skinstatus.acne                                                     0.634\n",
       "attributes.skinstatus.health                                                   9.525\n",
       "attributes.headpose.yaw_angle                                               0.004189\n",
       "attributes.headpose.pitch_angle                                            16.763775\n",
       "attributes.headpose.roll_angle                                              2.305076\n",
       "attributes.blur.blurness.threshold                                              50.0\n",
       "attributes.blur.blurness.value                                                 0.364\n",
       "attributes.blur.motionblur.threshold                                            50.0\n",
       "attributes.blur.motionblur.value                                               0.364\n",
       "attributes.blur.gaussianblur.threshold                                          50.0\n",
       "attributes.blur.gaussianblur.value                                             0.364\n",
       "attributes.smile.threshold                                                      50.0\n",
       "attributes.smile.value                                                         100.0\n",
       "attributes.eyestatus.left_eye_status.normal_gla...                             0.002\n",
       "attributes.eyestatus.left_eye_status.no_glass_e...                               0.0\n",
       "attributes.eyestatus.left_eye_status.occlusion                                   0.0\n",
       "attributes.eyestatus.left_eye_status.no_glass_e...                            99.998\n",
       "attributes.eyestatus.left_eye_status.normal_gla...                               0.0\n",
       "attributes.eyestatus.left_eye_status.dark_glasses                                0.0\n",
       "attributes.eyestatus.right_eye_status.normal_gl...                             0.031\n",
       "attributes.eyestatus.right_eye_status.no_glass_...                               0.0\n",
       "attributes.eyestatus.right_eye_status.occlusion                                0.008\n",
       "attributes.eyestatus.right_eye_status.no_glass_...                            99.961\n",
       "attributes.eyestatus.right_eye_status.normal_gl...                               0.0\n",
       "attributes.eyestatus.right_eye_status.dark_glasses                               0.0\n",
       "attributes.facequality.threshold                                                70.1\n",
       "attributes.facequality.value                                                  85.658\n",
       "attributes.eyegaze.right_eye_gaze.position_x_co...                             0.468\n",
       "attributes.eyegaze.right_eye_gaze.vector_z_comp...                             0.944\n",
       "attributes.eyegaze.right_eye_gaze.vector_x_comp...                            -0.054\n",
       "attributes.eyegaze.right_eye_gaze.vector_y_comp...                             0.325\n",
       "attributes.eyegaze.right_eye_gaze.position_y_co...                             0.443\n",
       "attributes.eyegaze.left_eye_gaze.position_x_coo...                             0.506\n",
       "attributes.eyegaze.left_eye_gaze.vector_z_compo...                             0.948\n",
       "attributes.eyegaze.left_eye_gaze.vector_x_compo...                             0.168\n",
       "attributes.eyegaze.left_eye_gaze.vector_y_compo...                             0.269\n",
       "attributes.eyegaze.left_eye_gaze.position_y_coo...                             0.436\n",
       "face_rectangle.width                                                             107\n",
       "face_rectangle.top                                                               108\n",
       "face_rectangle.left                                                              141\n",
       "face_rectangle.height                                                            107"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.json_normalize(r_analyze.json()['faces']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 人脸对比"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  上述人脸检测需要不断的重复利用，我们尝试将其封装成函数使用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=449x308 at 0x7FDF0923A390>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. 打开本地图片\n",
    "from PIL import Image\n",
    "Image.open('Liu_01.jpeg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<_io.BufferedReader name='Liu_01.jpeg'>\n"
     ]
    }
   ],
   "source": [
    "# 2. 打开本地图片二进制\n",
    "print(open('Liu_01.jpeg', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 准备工作\n",
    "import requests\n",
    "API_key = 'jRpCaZ34kqNYJ8Zppdc-yGGum_YkETov'\n",
    "API_sercret = 'Vr0PtRCw-ZFwYXTKVfi6aDNaSqlfunK3'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def face_detect(API_key,API_sercret,image_path):\n",
    "    \"\"\"该函数为调用face++ face_detect接口\"\"\"\n",
    "    base_url = 'https://api-cn.faceplusplus.com/facepp/v3/detect'\n",
    "    files = {'image_file': ('Liu_01.jpeg', open('Liu_01.jpeg', 'rb'), 'multipart/form-data')}\n",
    "    payload = {\n",
    "            'api_key':API_key,\n",
    "            'api_secret':API_sercret\n",
    "            }\n",
    "    \n",
    "\n",
    "    r = requests.post(base_url, params = payload,files = files )\n",
    "    return r.json()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'request_id': '1647161289,20debd3f-796b-420c-9398-bf8b2ad183c4',\n",
       " 'time_used': 88,\n",
       " 'faces': [{'face_token': 'a7185d5e00c3a4fb727f6fefe515d79b',\n",
       "   'face_rectangle': {'top': 98, 'left': 164, 'width': 118, 'height': 118}}],\n",
       " 'image_id': 'fwD9nHfTb9s3JIGwQOQmSA==',\n",
       " 'face_num': 1}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试\n",
    "face_detect(API_key,API_sercret,'Liu_01.jpeg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备人脸元数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'request_id': '1647161359,3b85f2c1-b40e-47a7-9774-d041dd8b44bf',\n",
       " 'time_used': 93,\n",
       " 'faces': [{'face_token': 'c9399d0f7dadbca3004657c62b79089e',\n",
       "   'face_rectangle': {'top': 98, 'left': 164, 'width': 118, 'height': 118}}],\n",
       " 'image_id': 'fwD9nHfTb9s3JIGwQOQmSA==',\n",
       " 'face_num': 1}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "face_detect(API_key,API_sercret,'Liu_01.jpeg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "Liu_meta_data = face_detect(API_key,API_sercret,'Liu_01.jpeg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4606ff0c8cf5c64e919a5d63169dce46'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Liu_meta_data_face_token = Liu_meta_data['faces'][0]['face_token']\n",
    "Liu_meta_data_face_token"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 给出人脸图片进行相似度查询"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'request_id': '1647161530,e97089a5-7745-46ad-9ded-330a8ac84ca2',\n",
       " 'time_used': 202,\n",
       " 'faces': [{'face_token': '0bce562b72d4fc622dca4864ff8f29bb',\n",
       "   'face_rectangle': {'top': 98, 'left': 164, 'width': 118, 'height': 118}}],\n",
       " 'image_id': 'fwD9nHfTb9s3JIGwQOQmSA==',\n",
       " 'face_num': 1}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. 准备测试人脸图片 face2\n",
    "\n",
    "face_detect(API_key,API_sercret,'Liu_02.jpeg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b218db3ff2a38cc01f58bf30f3f5cc09'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "face02_token = face_detect(API_key,API_sercret,'Liu_02.jpeg')['faces'][0]['face_token']\n",
    "face02_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. 人脸对比\n",
    "\n",
    "compare_url = 'https://api-cn.faceplusplus.com/facepp/v3/compare'\n",
    "\n",
    "payload = {\n",
    "    'api_key':API_key,\n",
    "    'api_secret':API_sercret,\n",
    "    'face_token1':Liu_meta_data_face_token,\n",
    "    'face_token2':face02_token\n",
    "}\n",
    "r = requests.post(compare_url,params = payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'request_id': '1647161725,5b4b05eb-99d1-48da-951c-62f9a6ff5520',\n",
       " 'time_used': 289,\n",
       " 'confidence': 97.084,\n",
       " 'thresholds': {'1e-3': 62.327, '1e-4': 69.101, '1e-5': 73.975}}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 成功！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 应用\n",
    "\n",
    "* 尝试设计宿舍人脸识别系统"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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